


'''simple docstring'''



def      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       str   =  len(lowerCamelCase_       )
			for i in range(1			,   lowerCamelCase_       ):
						UpperCAmelCase__	:       str   =  collection[i]
						UpperCAmelCase__	:       str   =  0
						UpperCAmelCase__	:       Tuple   =  i - 1

						while low <= high:
									UpperCAmelCase__	:       List[str]   =  (low + high) // 2
									if val < collection[mid]:
												UpperCAmelCase__	:       Dict   =  mid - 1
									else:
												UpperCAmelCase__	:       str   =  mid + 1
						for j in range(lowerCamelCase_			,   lowerCamelCase_			,   -1       ):
									UpperCAmelCase__	:       Tuple   =  collection[j - 1]
						UpperCAmelCase__	:       Tuple   =  val
			return collection


if __name__ == "__main__":
		__A						=input('Enter numbers separated by a comma:\n').strip()
		__A						=[int(item) for item in user_input.split(',')]
		print(binary_insertion_sort(unsorted))


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






